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Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling

Overview of attention for article published in Frontiers in Microbiology, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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1 blog
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16 X users
googleplus
1 Google+ user

Citations

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112 Dimensions

Readers on

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326 Mendeley
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Title
Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling
Published in
Frontiers in Microbiology, August 2016
DOI 10.3389/fmicb.2016.01174
Pubmed ID
Authors

Luis E. Escobar, Meggan E. Craft

Abstract

Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 326 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Poland 1 <1%
France 1 <1%
Peru 1 <1%
Unknown 321 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 57 17%
Student > Ph. D. Student 51 16%
Researcher 49 15%
Student > Bachelor 30 9%
Student > Doctoral Student 19 6%
Other 51 16%
Unknown 69 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 107 33%
Environmental Science 40 12%
Veterinary Science and Veterinary Medicine 26 8%
Biochemistry, Genetics and Molecular Biology 11 3%
Medicine and Dentistry 11 3%
Other 50 15%
Unknown 81 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2019.
All research outputs
#2,080,173
of 25,045,181 outputs
Outputs from Frontiers in Microbiology
#1,491
of 28,700 outputs
Outputs of similar age
#38,008
of 376,460 outputs
Outputs of similar age from Frontiers in Microbiology
#33
of 435 outputs
Altmetric has tracked 25,045,181 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 28,700 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 376,460 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 435 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.